Mapping Ash Tree Colonization in anAgricultural Moutain Landscape: Investigation

the Potential of Hyperspectral Imagery

D. Sheeren1,2, M. Fauvel1,2, S. Ladet2, A. Jacquin2, G. Bertoni1,2 andA. Gibon2

1 INP Toulouse - University of Toulouse2 UMR 1201 DYNAFOR, INRA/INP-ENSAT/INP-EI Purpan

2011 IEEE International Geoscience and Remote Sensing Symposium

24-29 July, Vancouver, Canada

MADONNA Project

Image classification

Experimental results

Conclusions and perspectives

MADONNA Project

Image classification

Experimental results

Conclusions and perspectives

Context of the work 1/3

Scientific context:

Landscape ecology:

I What is the impact of agricultural activities on the landscape?I What is the impact of landscape heterogeneity on the biodiversity?

Global change:

I How evolve landscape and biodiversity?I What are the factors of evolution?

Predict and anticipate the responses of ecosystems to landscape changes.

Context of the work 2/3What are the causes and the consequences of ash three colonization in thePyrenees mountain?

Villelongue village, 65260-France

1950 2000Up to now:

X Ecological process understood

X Multi-agents model build

× Accurate ash-thematic map

Context of the work 3/3

Current method: Aerial images + visual inspection + field survey

I Small geographical areaI Time consumingI Cost !

Multispectral satellite images are not enough spatially and spectrallyaccurate for ash detection

tree/not tree ok, but it is not possible to go to the species

Good multitemporal data are very difficult to obtain

Hypothesis: With hyperspectral images, it would be possible todifferentiate between ash tree and other species of tree

Madonna Project!

Context of the work 3/3

Current method: Aerial images + visual inspection + field survey

I Small geographical areaI Time consumingI Cost !

Multispectral satellite images are not enough spatially and spectrallyaccurate for ash detection

tree/not tree ok, but it is not possible to go to the species

Good multitemporal data are very difficult to obtain

Hypothesis: With hyperspectral images, it would be possible todifferentiate between ash tree and other species of tree

Madonna Project!

Madonna project: Objectives and data

Objectives:

• Mapping of the ash tree distribution• 2D et 3D information• Estimation of structural and biophysical parameters (tree density and height,

foliar chlorophyll . . . )

Data (summer 2010).

• Very high spatial resolution hyperspectral images• LiDar data• Field data (ash trees and other dominant species, foliar analysis . . . )

Area covered by the mission

Village of Villelongue, France (00◦03’W and 42◦57’N).Medium altitudinal range (450-1800m)

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Field data 1/2

Collecting tree species:

Field data 2/2

Biophysical parameters:

Density

Age

Diameter

Dominant height

Topography

Chlorophyll and nitrogen content

(GPS position)

Some ash examples

MADONNA Project

Image classification

Experimental results

Conclusions and perspectives

Very high spatial resolution hyperspectral images 1/2

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6

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HySpex sensor:

Spectral resolution : 1.5 nm, 400-1000 nm, 160 bands

Spatial resolution: 50 cm

Very high spatial resolution hyperspectral images 2/2

Pattern recognition approach:

Problem of the spectral dimensionality: statistical methods fail

Use of non-linear SVM (Gaussian kernel)

But the optimization of the hyperparameter is too demanding in terms oftime processing when using the conventional cross-validation strategy(about 3 To of data to process).

A fast and accurate method for optimizing the hyperparameter is neededfor an operational system

SVM + Kernel alignment

Very high spatial resolution hyperspectral images 2/2

Pattern recognition approach:

Problem of the spectral dimensionality: statistical methods fail

Use of non-linear SVM (Gaussian kernel)

But the optimization of the hyperparameter is too demanding in terms oftime processing when using the conventional cross-validation strategy(about 3 To of data to process).

A fast and accurate method for optimizing the hyperparameter is neededfor an operational system

SVM + Kernel alignment

Support Vector Machine

yi = 1

yi = −1

w

H (α, b) : {x|f (x) = 0}

Supervised method: S ={

(x1, y1), . . . , (xn, yn)}∈ Rd × {−1; 1}

Separating function: f (z) = sgn( n∑

i=1αik(z,xi) + b

)Solve QP problem:

maxα

g(α) =n∑

i=1αi −

12

n∑i,j=1

αiαjyiyjk(xi ,xj)

constraint to 0 ≤ αi ≤ C et∑n

i=1 αiyi = 0

Kernel functionKernel function k: similarity measure between two spectra xi et xj

Gaussian kernel : kg(xi ,xj) = exp(− ‖xi − xj‖2

2σ2

)σ2 is an hyperparameter that controls how two spectra are considered assimilar or not.

Ideally, σ2 must be tuned such as:

kg(xi ,xj) ≈ 1 If yi = yj

kg(xi ,xj) ≈ 0 Else

Ideal kernel matrix:

KI =

1 δy1y2 . . . δy1yn

δy2y1 1 . . . δy2yn

......

. . ....

δyny1 δyny2 . . . 1

Kernel alignment

Alignment A: compute the similitude (angle) between the ideal matrix andthe kernel matrix with parameter σ2

A(σ) = 〈K I ,K 〉F‖K I‖F‖K‖F

σ is selected such A(σ) is maximal

Contrary to cross-validation, there is no need to solve the QP problem

0 20 40 600.25

0.3

0.35

0.4

0.45

0.5

MADONNA Project

Image classification

Experimental results

Conclusions and perspectives

Protocol

Ground thruth:

12 tree species (Ash tree, Chestnut tree, Lime tree, Hazel tree . . . ).

Classification of the tree species: Are ash trees identifiable?

Classification of the image: Are the results spatially consistent?

ResultsQuantitative analysis: Ash tree separability

GMM SVM SVM-linOA 72% 94% 89%Kappa 0.65 0.92 0.89

Ash treeUser accuracy 84.0% 89.9% 83.1%Producer accuracy 53.6% 89.9% 88.8%

Qualitative analysis:

MADONNA Project

Image classification

Experimental results

Conclusions and perspectives

Conclusions and perspectives

Conclusions:

Accurate mapping of ash tree is possible with hyperspectral images

Framework: SVM + kernel alignment

Perspectives:

Spatial regularization

Can biophysical parameters be estimated?

Mapping Ash Tree Colonization in anAgricultural Moutain Landscape: Investigation

the Potential of Hyperspectral Imagery

D. Sheeren1,2, M. Fauvel1,2, S. Ladet2, A. Jacquin2, G. Bertoni1,2 andA. Gibon2

1 INP Toulouse - University of Toulouse2 UMR 1201 DYNAFOR, INRA/INP-ENSAT/INP-EI Purpan

2011 IEEE International Geoscience and Remote Sensing Symposium

24-29 July, Vancouver, Canada